Bias Correction in Machine Learning-based Classification of Rare Events
This addresses the challenge of rare event detection in text classification for online platforms, but it is incremental as it builds on existing methods with specific improvements.
The paper tackles the problem of classifying rare online platform businesses from web-scraped texts by developing a machine learning approach that reduces false positives, using calibrated probabilities and ensembles to greatly reduce bias in estimates.
Online platform businesses can be identified by using web-scraped texts. This is a classification problem that combines elements of natural language processing and rare event detection. Because online platforms are rare, accurately identifying them with Machine Learning algorithms is challenging. Here, we describe the development of a Machine Learning-based text classification approach that reduces the number of false positives as much as possible. It greatly reduces the bias in the estimates obtained by using calibrated probabilities and ensembles.